In recent years, cardiovascular diseases have become a leading cause of death all over the world; millions of people get the cardiovascular diseases and died. A cardiac function is an important indicator in the diagnosis of cardiovascular diseases. For example, quantitative analysis of global and regional measurements, such as ventricle volumes, ejection fraction, and wall thickness may help a radiologist improve the accuracy and efficiency of the heart function evaluation. A CAD scheme may be used for locating endocardial and epicardial boundaries of the left ventricle in 4D cardiac cine MR images.
However, there are great variations in gray-scale and gradient distributions in 4D cardiac magnetic resonance images of different patients, and a diversity of tissue adjacent to the epicardium of the left ventricle. Such variations may bring about difficulty in accurately detecting the epicardium and endocardium of the left ventricle.
Heart perfusion magnetic resonance imaging techniques are widely used in heart disease diagnosis. However, MR images may include distortion and/or artifacts, which may cause misdiagnosis.
Great variations may exist in gray-scale and gradient distributions in 4D cardiac magnetic resonance images, either obtained from a similar scanning region of different patients, or from different regions of a same patient. In addition, a simple threshold-based detection approach or gradient-based detection approach may be inadequate to accurately extract the left ventricle endocardium and epicardium, because of a variety of tissues adjacent to the epicardium of the left ventricle. A model-based detection approach may need a lot of training samples so as to obtain a satisfactory result suitable for automatic detection.
Thus, it is desirable to develop systems and methods for processing and correcting MR images, thereby improving the quality of cardiac MR images.